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fitc anti mouse cd3  (Elabscience Biotechnology)


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    Structured Review

    Elabscience Biotechnology fitc anti mouse cd3
    Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of <t>CD3</t> and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.
    Fitc Anti Mouse Cd3, supplied by Elabscience Biotechnology, used in various techniques. Bioz Stars score: 95/100, based on 45 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/fitc anti mouse cd3/product/Elabscience Biotechnology
    Average 95 stars, based on 45 article reviews
    fitc anti mouse cd3 - by Bioz Stars, 2026-06
    95/100 stars

    Images

    1) Product Images from "Engineered Salmonella -mediated c-di-AMP delivery activates STING to remodel the tumor microenvironment"

    Article Title: Engineered Salmonella -mediated c-di-AMP delivery activates STING to remodel the tumor microenvironment

    Journal: Molecular Therapy Oncology

    doi: 10.1016/j.omton.2026.201185

    Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.
    Figure Legend Snippet: Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.

    Techniques Used: Flow Cytometry



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    Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of <t>CD3</t> and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.
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    Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of <t>CD3</t> and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.
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    Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of <t>CD3</t> and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.
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    Knockdown of LDHA/B inhibits HCC growth by activating CD8 + T cell-mediated antitumor immunity (A) WB analysis of LDHA, LDHB, and β-actin (loading control) expression in control and LDHA/B DKO Hepa 1–6 cells. (B) Representative images of tumors dissected from tumor-bearing mice ( n = 6). Tumor weights (C), tumor growth curves (D), Kaplan-Meier survival curve ( n = 6) (E), and lactate contents in tumor tissues (F) of the control and LDHA/B DKO groups ( n = 6). (G) IHC staining of H3K18la and Pan Kla in tumor tissues (scale bars, 100 μm, n = 6). (H–M) FC analysis of tumor-infiltrating immune cells: TILs were isolated from control and LDHA/B-DKO tumors. FC plots (left) and quantitative statistics (right) show: the proportion of <t>CD3</t> + CD8 + CTLs (H), the proportion of CD44hiCD62Llo effector memory CTLs (I), and the proportions of intratumoral GzMB + (J), and IFN-γ + (K) CTLs with representative scatterplots ( n = 6). Cell aggregates and debris were first excluded, followed by gating on the target cell populations and markers of interest. Representative FC plots and quantitative data are presented. (L and M) Tumor size and growth curves of mice treated with anti-CD8 antibody to deplete CD8 + T cells or isotype control (IgG) ( n = 6). (N) Mouse body weights at the end of the experiment ( n = 6). Data are presented as mean ± SD. (C–F, and H–M) were analyzed using Student’s t test, and (N) was analyzed by two-way ANOVA. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001; ns, not significant. All experiments were repeated three times. β-actin served as the loading control for WB experiments.
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    Knockdown of LDHA/B inhibits HCC growth by activating CD8 + T cell-mediated antitumor immunity (A) WB analysis of LDHA, LDHB, and β-actin (loading control) expression in control and LDHA/B DKO Hepa 1–6 cells. (B) Representative images of tumors dissected from tumor-bearing mice ( n = 6). Tumor weights (C), tumor growth curves (D), Kaplan-Meier survival curve ( n = 6) (E), and lactate contents in tumor tissues (F) of the control and LDHA/B DKO groups ( n = 6). (G) IHC staining of H3K18la and Pan Kla in tumor tissues (scale bars, 100 μm, n = 6). (H–M) FC analysis of tumor-infiltrating immune cells: TILs were isolated from control and LDHA/B-DKO tumors. FC plots (left) and quantitative statistics (right) show: the proportion of <t>CD3</t> + CD8 + CTLs (H), the proportion of CD44hiCD62Llo effector memory CTLs (I), and the proportions of intratumoral GzMB + (J), and IFN-γ + (K) CTLs with representative scatterplots ( n = 6). Cell aggregates and debris were first excluded, followed by gating on the target cell populations and markers of interest. Representative FC plots and quantitative data are presented. (L and M) Tumor size and growth curves of mice treated with anti-CD8 antibody to deplete CD8 + T cells or isotype control (IgG) ( n = 6). (N) Mouse body weights at the end of the experiment ( n = 6). Data are presented as mean ± SD. (C–F, and H–M) were analyzed using Student’s t test, and (N) was analyzed by two-way ANOVA. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001; ns, not significant. All experiments were repeated three times. β-actin served as the loading control for WB experiments.
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    Knockdown of LDHA/B inhibits HCC growth by activating CD8 + T cell-mediated antitumor immunity (A) WB analysis of LDHA, LDHB, and β-actin (loading control) expression in control and LDHA/B DKO Hepa 1–6 cells. (B) Representative images of tumors dissected from tumor-bearing mice ( n = 6). Tumor weights (C), tumor growth curves (D), Kaplan-Meier survival curve ( n = 6) (E), and lactate contents in tumor tissues (F) of the control and LDHA/B DKO groups ( n = 6). (G) IHC staining of H3K18la and Pan Kla in tumor tissues (scale bars, 100 μm, n = 6). (H–M) FC analysis of tumor-infiltrating immune cells: TILs were isolated from control and LDHA/B-DKO tumors. FC plots (left) and quantitative statistics (right) show: the proportion of <t>CD3</t> + CD8 + CTLs (H), the proportion of CD44hiCD62Llo effector memory CTLs (I), and the proportions of intratumoral GzMB + (J), and IFN-γ + (K) CTLs with representative scatterplots ( n = 6). Cell aggregates and debris were first excluded, followed by gating on the target cell populations and markers of interest. Representative FC plots and quantitative data are presented. (L and M) Tumor size and growth curves of mice treated with anti-CD8 antibody to deplete CD8 + T cells or isotype control (IgG) ( n = 6). (N) Mouse body weights at the end of the experiment ( n = 6). Data are presented as mean ± SD. (C–F, and H–M) were analyzed using Student’s t test, and (N) was analyzed by two-way ANOVA. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001; ns, not significant. All experiments were repeated three times. β-actin served as the loading control for WB experiments.
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    Knockdown of LDHA/B inhibits HCC growth by activating CD8 + T cell-mediated antitumor immunity (A) WB analysis of LDHA, LDHB, and β-actin (loading control) expression in control and LDHA/B DKO Hepa 1–6 cells. (B) Representative images of tumors dissected from tumor-bearing mice ( n = 6). Tumor weights (C), tumor growth curves (D), Kaplan-Meier survival curve ( n = 6) (E), and lactate contents in tumor tissues (F) of the control and LDHA/B DKO groups ( n = 6). (G) IHC staining of H3K18la and Pan Kla in tumor tissues (scale bars, 100 μm, n = 6). (H–M) FC analysis of tumor-infiltrating immune cells: TILs were isolated from control and LDHA/B-DKO tumors. FC plots (left) and quantitative statistics (right) show: the proportion of <t>CD3</t> + CD8 + CTLs (H), the proportion of CD44hiCD62Llo effector memory CTLs (I), and the proportions of intratumoral GzMB + (J), and IFN-γ + (K) CTLs with representative scatterplots ( n = 6). Cell aggregates and debris were first excluded, followed by gating on the target cell populations and markers of interest. Representative FC plots and quantitative data are presented. (L and M) Tumor size and growth curves of mice treated with anti-CD8 antibody to deplete CD8 + T cells or isotype control (IgG) ( n = 6). (N) Mouse body weights at the end of the experiment ( n = 6). Data are presented as mean ± SD. (C–F, and H–M) were analyzed using Student’s t test, and (N) was analyzed by two-way ANOVA. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001; ns, not significant. All experiments were repeated three times. β-actin served as the loading control for WB experiments.
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    Image Search Results


    Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.

    Journal: Molecular Therapy Oncology

    Article Title: Engineered Salmonella -mediated c-di-AMP delivery activates STING to remodel the tumor microenvironment

    doi: 10.1016/j.omton.2026.201185

    Figure Lengend Snippet: Effect of SL disA therapy on T cells and macrophage from tumor samples (A, B) Flow Cytometry analysis of CD3 and CD8 surface markers(A) and CD3 + CD8 + cells statistical graph (B). (C, D) Flow cytometry analysis of CD3 and CD4 surface markers (C) and CD3 + CD4 + cells statistical graph (D). (E, F) Flow cytometry analysis of F4/80 and CD86 surface markers (E) and F4/80 + CD86 + cells statistical graph (F). Data are expressed as mean ± SEM, n = 3. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, one-way ANOVA with Tukey’s multiple comparisons tests.

    Article Snippet: The following antibodies were used: FITC anti-mouse F4/80 (clone CI: A3-1), APC anti-mouse CD86 (clone GL-1), FITC anti-mouse CD3 (clone 17A2), APC anti-mouse CD4 (clone GK1.5), and APC anti-mouse CD8 (clone YTS-169), all purchased from Elabscience Biotechnology Co., Ltd.

    Techniques: Flow Cytometry

    Knockdown of LDHA/B inhibits HCC growth by activating CD8 + T cell-mediated antitumor immunity (A) WB analysis of LDHA, LDHB, and β-actin (loading control) expression in control and LDHA/B DKO Hepa 1–6 cells. (B) Representative images of tumors dissected from tumor-bearing mice ( n = 6). Tumor weights (C), tumor growth curves (D), Kaplan-Meier survival curve ( n = 6) (E), and lactate contents in tumor tissues (F) of the control and LDHA/B DKO groups ( n = 6). (G) IHC staining of H3K18la and Pan Kla in tumor tissues (scale bars, 100 μm, n = 6). (H–M) FC analysis of tumor-infiltrating immune cells: TILs were isolated from control and LDHA/B-DKO tumors. FC plots (left) and quantitative statistics (right) show: the proportion of CD3 + CD8 + CTLs (H), the proportion of CD44hiCD62Llo effector memory CTLs (I), and the proportions of intratumoral GzMB + (J), and IFN-γ + (K) CTLs with representative scatterplots ( n = 6). Cell aggregates and debris were first excluded, followed by gating on the target cell populations and markers of interest. Representative FC plots and quantitative data are presented. (L and M) Tumor size and growth curves of mice treated with anti-CD8 antibody to deplete CD8 + T cells or isotype control (IgG) ( n = 6). (N) Mouse body weights at the end of the experiment ( n = 6). Data are presented as mean ± SD. (C–F, and H–M) were analyzed using Student’s t test, and (N) was analyzed by two-way ANOVA. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001; ns, not significant. All experiments were repeated three times. β-actin served as the loading control for WB experiments.

    Journal: iScience

    Article Title: Targeting KIF20A blocks lactylation modification to suppress immune escape in hepatocellular carcinoma

    doi: 10.1016/j.isci.2026.115372

    Figure Lengend Snippet: Knockdown of LDHA/B inhibits HCC growth by activating CD8 + T cell-mediated antitumor immunity (A) WB analysis of LDHA, LDHB, and β-actin (loading control) expression in control and LDHA/B DKO Hepa 1–6 cells. (B) Representative images of tumors dissected from tumor-bearing mice ( n = 6). Tumor weights (C), tumor growth curves (D), Kaplan-Meier survival curve ( n = 6) (E), and lactate contents in tumor tissues (F) of the control and LDHA/B DKO groups ( n = 6). (G) IHC staining of H3K18la and Pan Kla in tumor tissues (scale bars, 100 μm, n = 6). (H–M) FC analysis of tumor-infiltrating immune cells: TILs were isolated from control and LDHA/B-DKO tumors. FC plots (left) and quantitative statistics (right) show: the proportion of CD3 + CD8 + CTLs (H), the proportion of CD44hiCD62Llo effector memory CTLs (I), and the proportions of intratumoral GzMB + (J), and IFN-γ + (K) CTLs with representative scatterplots ( n = 6). Cell aggregates and debris were first excluded, followed by gating on the target cell populations and markers of interest. Representative FC plots and quantitative data are presented. (L and M) Tumor size and growth curves of mice treated with anti-CD8 antibody to deplete CD8 + T cells or isotype control (IgG) ( n = 6). (N) Mouse body weights at the end of the experiment ( n = 6). Data are presented as mean ± SD. (C–F, and H–M) were analyzed using Student’s t test, and (N) was analyzed by two-way ANOVA. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001; ns, not significant. All experiments were repeated three times. β-actin served as the loading control for WB experiments.

    Article Snippet: FITC Anti-Mouse CD3 Antibody , Elabscience , Cat# E-AB-F1013C; RRID: AB_3065041.

    Techniques: Knockdown, Control, Expressing, Immunohistochemistry, Isolation